pls: Fit Partial Least Squares Structural Equation Models

In plssem: Complex Partial Least Squares Structural Equation Modeling

Fit Partial Least Squares Structural Equation Models

Description

'pls()' estimates Partial Least Squares Structural Equation Models (PLS-SEM) and their consistent (PLSc) variants. The function accepts 'lavaan'-style syntax, handles ordered indicators through polychoric correlations and probit factor scores, and supports multilevel specifications expressed with 'lme4'-style random effects terms inside the structural model.

Usage

pls(
  syntax,
  data,
  standardize = TRUE,
  consistent = TRUE,
  bootstrap = FALSE,
  sample = 50L,
  ordered = NULL,
  mcpls = NULL,
  probit = NULL,
  tolerance = 1e-05,
  max.iter.0_5 = 100L,
  mc.min.iter = 5L,
  mc.max.iter = 250L,
  mc.reps = 20000L,
  mc.tol = 0.001,
  mc.fixed.seed = FALSE,
  mc.polyak.juditsky = FALSE,
  mc.fn.args = list(),
  verbose = interactive(),
  ...
)

Arguments

syntax	Character string with 'lavaan'-style model syntax describing both measurement ('=~') and structural ('~') relations. Random effects are specified with '(term | cluster)' statements.
data	A 'data.frame' or coercible object containing the manifest indicators referenced in 'syntax'. Ordered factors are automatically detected, but can also be supplied explicitly through 'ordered'.
standardize	Logical; if 'TRUE', indicators are standardized before estimation so that factor scores have comparable scales.
consistent	Logical; 'TRUE' requests PLSc corrections, whereas 'FALSE' fits the traditional PLS model.
bootstrap	Logical; if 'TRUE', nonparametric bootstrap standard errors are computed with 'sample' resamples.
sample	Integer giving the number of bootstrap resamples drawn when 'bootstrap = TRUE'.
ordered	Optional character vector naming manifest indicators that should be treated as ordered when computing polychoric correlations.
mcpls	Should a Monte-Carlo consistency correction be applied?
probit	Logical; overrides the automatic choice of probit factor scores that is based on whether ordered indicators are present.
tolerance	Numeric; Convergence criteria/tolerance.
max.iter.0_5	Maximum number of PLS iterations performed when estimating the measurement and structural models.
mc.min.iter	Minimum number of iterations in MC-PLS algorithm.
mc.max.iter	Maximum number of iterations in MC-PLS algorithm.
mc.reps	Monte-Carlo sample size in MC-PLS algorithm.
mc.tol	Tolerance in MC-PLS algorithm.
mc.fixed.seed	Should a fixed seed be used in the MC-PLS algorithm?
mc.polyak.juditsky	Should the polyak.juditsky running average method be applied in the MC-PLS algorithm?
mc.fn.args	Additional arguments to MC-PLS algorithm, mainly for controling the step size.
verbose	Should verbose output be printed?
...	Currently unused, reserved for future extensions.

Value

An object of class 'plssem' containing the estimated parameters, fit measures, factor scores, and any bootstrap results. Methods such as 'summary()', 'print()', and 'coef()' can be applied to inspect the fit.

See Also

[summary.plssem()], [print.plssem()]

Examples

# Linear Model with Continuous Data


library(plssem)
library(modsem)

tpb <- '
# Outer Model (Based on Hagger et al., 2007)
  ATT =~ att1 + att2 + att3 + att4 + att5
  SN =~ sn1 + sn2
  PBC =~ pbc1 + pbc2 + pbc3
  INT =~ int1 + int2 + int3
  BEH =~ b1 + b2

# Inner Model (Based on Steinmetz et al., 2011)
  INT ~ ATT + SN + PBC
  BEH ~ INT + PBC
'

fit <- pls(tpb, TPB, bootstrap = TRUE)
summary(fit)

# Linear Model with Ordered Data
tpb <- '
# Outer Model (Based on Hagger et al., 2007)
  ATT =~ att1 + att2 + att3 + att4 + att5
  SN =~ sn1 + sn2
  PBC =~ pbc1 + pbc2 + pbc3
  INT =~ int1 + int2 + int3
  BEH =~ b1 + b2

# Inner Model (Based on Steinmetz et al., 2011)
  INT ~ ATT + SN + PBC
  BEH ~ INT + PBC
'

fit <- pls(tpb, TPB_Ordered, bootstrap = TRUE)
summary(fit)

# Multilevel Random Slopes Model with Continuous Data
syntax <- "
  X =~ x1 + x2 + x3
  Z =~ z1 + z2 + z3
  Y =~ y1 + y2 + y3
  W =~ w1 + w2 + w3
  Y ~ X + Z + (1 + X + Z | cluster)
  W ~ X + Z + (1 + X + Z | cluster)
"

fit <- pls(syntax, data = randomSlopes, bootstrap = TRUE)
summary(fit)

# Multilevel Random Slopes Model with Ordered Data
syntax <- "
  X =~ x1 + x2 + x3
  Z =~ z1 + z2 + z3
  Y =~ y1 + y2 + y3
  W =~ w1 + w2 + w3
  Y ~ X + Z + (1 + X + Z | cluster)
  W ~ X + Z + (1 + X + Z | cluster)
"

fit <- pls(syntax, data = randomSlopesOrdered, bootstrap = TRUE)
summary(fit)

# Multilevel Random Intercepts Model with Continuous Data
syntax <- '
  f =~ y1 + y2 + y3
  f ~ x1 + x2 + x3 + w1 + w2 + (1 | cluster)
'

fit <- pls(syntax, data = randomIntercepts, bootstrap = TRUE)
summary(fit)

# Multilevel Random Intercepts Model with Ordered Data
syntax <- '
  f =~ y1 + y2 + y3
  f ~ x1 + x2 + x3 + w1 + w2 + (1 | cluster)
'

fit <- pls(syntax, data = randomInterceptsOrdered, bootstrap = TRUE)
summary(fit)

# Interaction Model with Continuous Data
m <- '
  X =~ x1 + x2 + x3
  Z =~ z1 + z2 + z3
  Y =~ y1 + y2 + y3

  Y ~ X + Z + X:Z
'

fit <- pls(m, modsem::oneInt, bootstrap = TRUE)
summary(fit)

# Interaction Model with Ordered Data
m <- '
  X =~ x1 + x2 + x3
  Z =~ z1 + z2 + z3
  Y =~ y1 + y2 + y3

  Y ~ X + Z + X:Z
'

fit <- pls(m, oneIntOrdered, bootstrap = TRUE)
summary(fit)

plssem documentation built on March 23, 2026, 5:08 p.m.